R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9.9,8.2,9.8,8,9.3,7.5,8.3,6.8,8,6.5,8.5,6.6,10.4,7.6,11.1,8,10.9,8.1,10,7.7,9.2,7.5,9.2,7.6,9.5,7.8,9.6,7.8,9.5,7.8,9.1,7.5,8.9,7.5,9,7.1,10.1,7.5,10.3,7.5,10.2,7.6,9.6,7.7,9.2,7.7,9.3,7.9,9.4,8.1,9.4,8.2,9.2,8.2,9,8.2,9,7.9,9,7.3,9.8,6.9,10,6.6,9.8,6.7,9.3,6.9,9,7,9,7.1,9.1,7.2,9.1,7.1,9.1,6.9,9.2,7,8.8,6.8,8.3,6.4,8.4,6.7,8.1,6.6,7.7,6.4,7.9,6.3,7.9,6.2,8,6.5,7.9,6.8,7.6,6.8,7.1,6.4,6.8,6.1,6.5,5.8,6.9,6.1,8.2,7.2,8.7,7.3,8.3,6.9,7.9,6.1,7.5,5.8,7.8,6.2),dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
WLMan WLVrouw
1 8.2 9.9
2 8.0 9.8
3 7.5 9.3
4 6.8 8.3
5 6.5 8.0
6 6.6 8.5
7 7.6 10.4
8 8.0 11.1
9 8.1 10.9
10 7.7 10.0
11 7.5 9.2
12 7.6 9.2
13 7.8 9.5
14 7.8 9.6
15 7.8 9.5
16 7.5 9.1
17 7.5 8.9
18 7.1 9.0
19 7.5 10.1
20 7.5 10.3
21 7.6 10.2
22 7.7 9.6
23 7.7 9.2
24 7.9 9.3
25 8.1 9.4
26 8.2 9.4
27 8.2 9.2
28 8.2 9.0
29 7.9 9.0
30 7.3 9.0
31 6.9 9.8
32 6.6 10.0
33 6.7 9.8
34 6.9 9.3
35 7.0 9.0
36 7.1 9.0
37 7.2 9.1
38 7.1 9.1
39 6.9 9.1
40 7.0 9.2
41 6.8 8.8
42 6.4 8.3
43 6.7 8.4
44 6.6 8.1
45 6.4 7.7
46 6.3 7.9
47 6.2 7.9
48 6.5 8.0
49 6.8 7.9
50 6.8 7.6
51 6.4 7.1
52 6.1 6.8
53 5.8 6.5
54 6.1 6.9
55 7.2 8.2
56 7.3 8.7
57 6.9 8.3
58 6.1 7.9
59 5.8 7.5
60 6.2 7.8
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WLVrouw
2.4612 0.5257
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.11847 -0.29705 -0.06187 0.29439 1.00726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.46122 0.50497 4.874 8.85e-06 ***
WLVrouw 0.52572 0.05644 9.315 4.03e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4275 on 58 degrees of freedom
Multiple R-squared: 0.5993, Adjusted R-squared: 0.5924
F-statistic: 86.76 on 1 and 58 DF, p-value: 4.028e-13
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.009649902 0.019299805 0.990350098
[2,] 0.030743342 0.061486684 0.969256658
[3,] 0.315793841 0.631587682 0.684206159
[4,] 0.325157403 0.650314805 0.674842597
[5,] 0.222110580 0.444221161 0.777889420
[6,] 0.140750102 0.281500203 0.859249898
[7,] 0.099506592 0.199013185 0.900493408
[8,] 0.080158473 0.160316946 0.919841527
[9,] 0.068075402 0.136150805 0.931924598
[10,] 0.050345346 0.100690692 0.949654654
[11,] 0.040080105 0.080160210 0.959919895
[12,] 0.026184277 0.052368555 0.973815723
[13,] 0.019888461 0.039776922 0.980111539
[14,] 0.013249309 0.026498618 0.986750691
[15,] 0.011705572 0.023411143 0.988294428
[16,] 0.012465050 0.024930100 0.987534950
[17,] 0.008530844 0.017061688 0.991469156
[18,] 0.005205515 0.010411029 0.994794485
[19,] 0.004673224 0.009346447 0.995326776
[20,] 0.006977506 0.013955013 0.993022494
[21,] 0.018267727 0.036535454 0.981732273
[22,] 0.060941704 0.121883409 0.939058296
[23,] 0.216471048 0.432942096 0.783528952
[24,] 0.635637128 0.728725744 0.364362872
[25,] 0.861483903 0.277032193 0.138516097
[26,] 0.865893135 0.268213729 0.134106865
[27,] 0.918047101 0.163905798 0.081952899
[28,] 0.989385898 0.021228204 0.010614102
[29,] 0.998046608 0.003906785 0.001953392
[30,] 0.998006080 0.003987840 0.001993920
[31,] 0.996759607 0.006480787 0.003240393
[32,] 0.994569601 0.010860798 0.005430399
[33,] 0.991313372 0.017373255 0.008686628
[34,] 0.985996396 0.028007208 0.014003604
[35,] 0.981013141 0.037973718 0.018986859
[36,] 0.972583706 0.054832589 0.027416294
[37,] 0.962961390 0.074077220 0.037038610
[38,] 0.965287787 0.069424425 0.034712213
[39,] 0.948518809 0.102962382 0.051481191
[40,] 0.922052090 0.155895819 0.077947910
[41,] 0.884530722 0.230938556 0.115469278
[42,] 0.862395795 0.275208409 0.137604205
[43,] 0.867195977 0.265608047 0.132804023
[44,] 0.818589222 0.362821557 0.181410778
[45,] 0.752482650 0.495034701 0.247517350
[46,] 0.734719016 0.530561968 0.265280984
[47,] 0.687863341 0.624273318 0.312136659
[48,] 0.614655927 0.770688147 0.385344073
[49,] 0.551146586 0.897706829 0.448853414
[50,] 0.824095727 0.351808545 0.175904273
[51,] 0.967679830 0.064640341 0.032320170
> postscript(file="/var/www/html/rcomp/tmp/1m1l31258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hf721258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3bppn1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ewu61258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53p3d1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.53410406 0.38667650 0.14953873 -0.02473683 -0.16701950 -0.32988172
7 8 9 10 11 12
-0.32875816 -0.29676527 -0.09162038 -0.01846838 0.20211117 0.30211117
13 14 15 16 17 18
0.34439384 0.29182139 0.34439384 0.25468361 0.35982850 -0.09274394
19 20 21 22 23 24
-0.27104083 -0.37618572 -0.22361327 0.19182139 0.40211117 0.54953873
25 26 27 28 29 30
0.69696628 0.79696628 0.90211117 1.00725606 0.70725606 0.10725606
31 32 33 34 35 36
-0.71332350 -1.11846838 -0.91332350 -0.45046127 -0.19274394 -0.09274394
37 38 39 40 41 42
-0.04531639 -0.14531639 -0.34531639 -0.29788883 -0.28759905 -0.42473683
43 44 45 46 47 48
-0.17730927 -0.11959194 -0.10930216 -0.31444705 -0.41444705 -0.16701950
49 50 51 52 53 54
0.18555295 0.34327028 0.20613250 0.06384984 -0.07843283 0.01127739
55 56 57 58 59 60
0.42783561 0.26497339 0.07526317 -0.51444705 -0.60415727 -0.36187461
> postscript(file="/var/www/html/rcomp/tmp/6e3z31258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.53410406 NA
1 0.38667650 0.53410406
2 0.14953873 0.38667650
3 -0.02473683 0.14953873
4 -0.16701950 -0.02473683
5 -0.32988172 -0.16701950
6 -0.32875816 -0.32988172
7 -0.29676527 -0.32875816
8 -0.09162038 -0.29676527
9 -0.01846838 -0.09162038
10 0.20211117 -0.01846838
11 0.30211117 0.20211117
12 0.34439384 0.30211117
13 0.29182139 0.34439384
14 0.34439384 0.29182139
15 0.25468361 0.34439384
16 0.35982850 0.25468361
17 -0.09274394 0.35982850
18 -0.27104083 -0.09274394
19 -0.37618572 -0.27104083
20 -0.22361327 -0.37618572
21 0.19182139 -0.22361327
22 0.40211117 0.19182139
23 0.54953873 0.40211117
24 0.69696628 0.54953873
25 0.79696628 0.69696628
26 0.90211117 0.79696628
27 1.00725606 0.90211117
28 0.70725606 1.00725606
29 0.10725606 0.70725606
30 -0.71332350 0.10725606
31 -1.11846838 -0.71332350
32 -0.91332350 -1.11846838
33 -0.45046127 -0.91332350
34 -0.19274394 -0.45046127
35 -0.09274394 -0.19274394
36 -0.04531639 -0.09274394
37 -0.14531639 -0.04531639
38 -0.34531639 -0.14531639
39 -0.29788883 -0.34531639
40 -0.28759905 -0.29788883
41 -0.42473683 -0.28759905
42 -0.17730927 -0.42473683
43 -0.11959194 -0.17730927
44 -0.10930216 -0.11959194
45 -0.31444705 -0.10930216
46 -0.41444705 -0.31444705
47 -0.16701950 -0.41444705
48 0.18555295 -0.16701950
49 0.34327028 0.18555295
50 0.20613250 0.34327028
51 0.06384984 0.20613250
52 -0.07843283 0.06384984
53 0.01127739 -0.07843283
54 0.42783561 0.01127739
55 0.26497339 0.42783561
56 0.07526317 0.26497339
57 -0.51444705 0.07526317
58 -0.60415727 -0.51444705
59 -0.36187461 -0.60415727
60 NA -0.36187461
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.38667650 0.53410406
[2,] 0.14953873 0.38667650
[3,] -0.02473683 0.14953873
[4,] -0.16701950 -0.02473683
[5,] -0.32988172 -0.16701950
[6,] -0.32875816 -0.32988172
[7,] -0.29676527 -0.32875816
[8,] -0.09162038 -0.29676527
[9,] -0.01846838 -0.09162038
[10,] 0.20211117 -0.01846838
[11,] 0.30211117 0.20211117
[12,] 0.34439384 0.30211117
[13,] 0.29182139 0.34439384
[14,] 0.34439384 0.29182139
[15,] 0.25468361 0.34439384
[16,] 0.35982850 0.25468361
[17,] -0.09274394 0.35982850
[18,] -0.27104083 -0.09274394
[19,] -0.37618572 -0.27104083
[20,] -0.22361327 -0.37618572
[21,] 0.19182139 -0.22361327
[22,] 0.40211117 0.19182139
[23,] 0.54953873 0.40211117
[24,] 0.69696628 0.54953873
[25,] 0.79696628 0.69696628
[26,] 0.90211117 0.79696628
[27,] 1.00725606 0.90211117
[28,] 0.70725606 1.00725606
[29,] 0.10725606 0.70725606
[30,] -0.71332350 0.10725606
[31,] -1.11846838 -0.71332350
[32,] -0.91332350 -1.11846838
[33,] -0.45046127 -0.91332350
[34,] -0.19274394 -0.45046127
[35,] -0.09274394 -0.19274394
[36,] -0.04531639 -0.09274394
[37,] -0.14531639 -0.04531639
[38,] -0.34531639 -0.14531639
[39,] -0.29788883 -0.34531639
[40,] -0.28759905 -0.29788883
[41,] -0.42473683 -0.28759905
[42,] -0.17730927 -0.42473683
[43,] -0.11959194 -0.17730927
[44,] -0.10930216 -0.11959194
[45,] -0.31444705 -0.10930216
[46,] -0.41444705 -0.31444705
[47,] -0.16701950 -0.41444705
[48,] 0.18555295 -0.16701950
[49,] 0.34327028 0.18555295
[50,] 0.20613250 0.34327028
[51,] 0.06384984 0.20613250
[52,] -0.07843283 0.06384984
[53,] 0.01127739 -0.07843283
[54,] 0.42783561 0.01127739
[55,] 0.26497339 0.42783561
[56,] 0.07526317 0.26497339
[57,] -0.51444705 0.07526317
[58,] -0.60415727 -0.51444705
[59,] -0.36187461 -0.60415727
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.38667650 0.53410406
2 0.14953873 0.38667650
3 -0.02473683 0.14953873
4 -0.16701950 -0.02473683
5 -0.32988172 -0.16701950
6 -0.32875816 -0.32988172
7 -0.29676527 -0.32875816
8 -0.09162038 -0.29676527
9 -0.01846838 -0.09162038
10 0.20211117 -0.01846838
11 0.30211117 0.20211117
12 0.34439384 0.30211117
13 0.29182139 0.34439384
14 0.34439384 0.29182139
15 0.25468361 0.34439384
16 0.35982850 0.25468361
17 -0.09274394 0.35982850
18 -0.27104083 -0.09274394
19 -0.37618572 -0.27104083
20 -0.22361327 -0.37618572
21 0.19182139 -0.22361327
22 0.40211117 0.19182139
23 0.54953873 0.40211117
24 0.69696628 0.54953873
25 0.79696628 0.69696628
26 0.90211117 0.79696628
27 1.00725606 0.90211117
28 0.70725606 1.00725606
29 0.10725606 0.70725606
30 -0.71332350 0.10725606
31 -1.11846838 -0.71332350
32 -0.91332350 -1.11846838
33 -0.45046127 -0.91332350
34 -0.19274394 -0.45046127
35 -0.09274394 -0.19274394
36 -0.04531639 -0.09274394
37 -0.14531639 -0.04531639
38 -0.34531639 -0.14531639
39 -0.29788883 -0.34531639
40 -0.28759905 -0.29788883
41 -0.42473683 -0.28759905
42 -0.17730927 -0.42473683
43 -0.11959194 -0.17730927
44 -0.10930216 -0.11959194
45 -0.31444705 -0.10930216
46 -0.41444705 -0.31444705
47 -0.16701950 -0.41444705
48 0.18555295 -0.16701950
49 0.34327028 0.18555295
50 0.20613250 0.34327028
51 0.06384984 0.20613250
52 -0.07843283 0.06384984
53 0.01127739 -0.07843283
54 0.42783561 0.01127739
55 0.26497339 0.42783561
56 0.07526317 0.26497339
57 -0.51444705 0.07526317
58 -0.60415727 -0.51444705
59 -0.36187461 -0.60415727
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/779la1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8t04f1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9p0tn1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w4kz1258731470.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11qldt1258731470.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12sjrt1258731470.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/131sf11258731470.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/147wq41258731470.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15wsrw1258731470.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16iy9o1258731470.tab")
+ }
>
> system("convert tmp/1m1l31258731470.ps tmp/1m1l31258731470.png")
> system("convert tmp/2hf721258731470.ps tmp/2hf721258731470.png")
> system("convert tmp/3bppn1258731470.ps tmp/3bppn1258731470.png")
> system("convert tmp/4ewu61258731470.ps tmp/4ewu61258731470.png")
> system("convert tmp/53p3d1258731470.ps tmp/53p3d1258731470.png")
> system("convert tmp/6e3z31258731470.ps tmp/6e3z31258731470.png")
> system("convert tmp/779la1258731470.ps tmp/779la1258731470.png")
> system("convert tmp/8t04f1258731470.ps tmp/8t04f1258731470.png")
> system("convert tmp/9p0tn1258731470.ps tmp/9p0tn1258731470.png")
> system("convert tmp/10w4kz1258731470.ps tmp/10w4kz1258731470.png")
>
>
> proc.time()
user system elapsed
2.476 1.578 2.861